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Dynamic Multi-object Gaussian Process Models

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)

Abstract

Statistical shape models (SSMs) are state-of-the-art medical image analysis tools for extracting and explaining shape across a set of biological structures. A combined analysis of shape and pose variation would provide additional utility in medical image analysis tasks such as automated multi-organ segmentation and completion of partial data. However, a principled and robust way to combine shape and pose features has been illusive due to three main issues: 1) non-homogeneity of the data (data with linear and non-linear natural variation across features), 2) non-optimal representation of the 3D Euclidean motion (rigid transformation representations that are not proportional to the kinetic energy that moves an object from one position to the other), and 3) artificial discretization of the models. Here, we propose a new dynamic multi-object statistical modelling framework for the analysis of human joints in a continuous domain. Specifically, we propose to normalise shape and dynamic spatial features in the same linearized statistical space, permitting the use of linear statistics; and we adopt an optimal 3D Euclidean motion representation for more accurate rigid transformation comparisons. The method affords an efficient generative dynamic multi-object modelling platform for biological joints. We validate the method using controlled synthetic data. The shape-pose prediction results suggest that the novel concept may have utility for a range of medical image analysis applications including management of human joint disorders.

This work is based on research supported by the National Research Foundation (NRF) of South Africa (grant no’s 105950 and 114393); the South African Research Chairs Initiative of the NRF and the Department of Science and Technology (grant no 98788); the South African Medical Research Council and the French Ministry of Higher Education, Research and Innovation (MESRI), Brest Métrople, France (grant no 17-178).

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References

  1. Ambellan, F., Zachow, S., von Tycowicz, C.: A surface-theoretic approach for statistical shape modeling. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 21–29. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32251-9_3

    Chapter  Google Scholar 

  2. Blanc, R., Székely, G.: Confidence regions for statistical model based shape prediction from sparse observations. IEEE Trans. Med. Imaging 31(6), 1300–1310 (2012)

    Article  Google Scholar 

  3. Bossa, M.N., Olmos, S.: Multi-object statistical pose+ shape models. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1204–1207. IEEE (2007)

    Google Scholar 

  4. Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Training models of shape from sets of examples. In: Hogg, D., Boyle, R. (eds.) BMVC92, pp. 9–18. Springer, London (1992). https://doi.org/10.1007/978-1-4471-3201-1_2

  5. Fletcher, P.T., Lu, C., Pizer, S.M., Joshi, S.: Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE Trans. Med. Imaging 23(8), 995–1005 (2004)

    Article  Google Scholar 

  6. Fouefack, J.R., Alemneh, T., Borotikar, B., Burdin, V., Douglas, T.S., Mutsvangwa, T.: Statistical shape-kinematics models of the skeletal joints: application to the shoulder complex. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 4815–4818. IEEE (2019)

    Google Scholar 

  7. Gee, A.H., Treece, G.M.: Systematic misregistration and the statistical analysis of surface data. Med. Image Anal. 18(2), 385–393 (2014)

    Article  Google Scholar 

  8. Lüthi, M., Gerig, T., Jud, C., Vetter, T.: Gaussian process morphable models. IEEE Trans. Pattern Anal. Mach. Intell. 40(8), 1860–1873 (2017)

    Article  Google Scholar 

  9. Moreau, B., Gilles, B., Jolivet, E., Petit, P., Subsol, G.: A new metric for statistical analysis of rigid transformations: application to the rib cage. In: Cardoso, M.J., et al. (eds.) GRAIL/MFCA/MICGen -2017. LNCS, vol. 10551, pp. 114–124. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67675-3_11

    Chapter  Google Scholar 

  10. Mutsvangwa, T., Burdin, V., Schwartz, C., Roux, C.: An automated statistical shape model developmental pipeline: application to the human scapula and humerus. IEEE Trans. Biomed. Eng. 62(4), 1098–1107 (2015)

    Article  Google Scholar 

  11. Schönborn, S., Egger, B., Morel-Forster, A., Vetter, T.: Markov chain monte carlo for automated face image analysis. Int. J. Comput. Vision 123(2), 160–183 (2017)

    Article  MathSciNet  Google Scholar 

  12. von Tycowicz, C., Ambellan, F., Mukhopadhyay, A., Zachow, S.: An efficient riemannian statistical shape model using differential coordinates: with application to the classification of data from the osteoarthritis initiative. Med. Image Anal. 43, 1–9 (2018)

    Article  Google Scholar 

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Correspondence to Jean-Rassaire Fouefack .

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Fouefack, JR., Borotikar, B., Douglas, T.S., Burdin, V., Mutsvangwa, T.E.M. (2020). Dynamic Multi-object Gaussian Process Models. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12264. Springer, Cham. https://doi.org/10.1007/978-3-030-59719-1_73

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  • DOI: https://doi.org/10.1007/978-3-030-59719-1_73

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